Builds type-safe, composable, and optimized LLM applications using the programmatic DSPy paradigm in Ruby.
DSPy.rb enables Ruby developers to program LLMs rather than manually engineering prompts, bringing structure and predictability to AI-powered features. By defining requirements through type-safe signatures and composable modules, developers can build complex multi-step workflows, deploy agents with tool-use capabilities, and integrate with multiple providers like Anthropic, OpenAI, and Ollama. This skill provides the architectural patterns and templates needed to move LLM logic from fragile strings into version-controlled, testable, and optimizable Ruby code.
Key Features
01Multi-provider configuration for OpenAI, Anthropic, Gemini, and Ollama
020 GitHub stars
03Composable modules for building complex, multi-step AI pipelines
04Support for Predict, ChainOfThought, ReAct, and CodeAct predictors
05Automatic prompt optimization using MIPROv2 techniques
06Type-safe signatures for LLM operations with runtime validation
Use Cases
01Creating reliable data extraction and classification pipelines in Rails
02Optimizing AI prompts programmatically through testing and evaluation
03Building autonomous tool-using agents for complex task execution
